@article {HeiningENEURO.0299-18.2019,
author = {Heining, Katharina and Kilias, Antje and Janz, Philipp and H{\"a}ussler, Ute and Kumar, Arvind and Haas, Carola A. and Egert, Ulrich},
title = {Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice},
volume = {6},
number = {5},
elocation-id = {ENEURO.0299-18.2019},
year = {2019},
doi = {10.1523/ENEURO.0299-18.2019},
publisher = {Society for Neuroscience},
abstract = {Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity during seizures is only one extreme. The aim of this study was to differentiate between different types of epileptiform activity (EA) patterns and investigate their temporal succession and interactions. We analyzed local field potentials (LFPs) from freely behaving male mice that had received an intrahippocampal kainate injection to model mesial temporal lobe epilepsy (MTLE). Epileptiform spikes occurred in distinct bursts. Using machine learning, we derived a scale reflecting the spike load of bursts and three main burst categories that we labeled high-load, medium-load, and low-load bursts. We found that bursts of these categories were non-randomly distributed in time. High-load bursts formed clusters and were typically surrounded by transition phases with increased rates of medium-load and low-load bursts. In apparent contradiction to this, increased rates of low-load bursts were also associated with longer background phases, i.e., periods lacking high-load bursting. Furthermore, the rate of low-load bursts was more strongly correlated with the duration of background phases than the overall rate of epileptiform spikes. Our findings are consistent with the hypothesis that low-level EA could promote network stability but could also participate in transitions towards major epileptiform events, depending on the current state of the network.},
URL = {https://www.eneuro.org/content/6/5/ENEURO.0299-18.2019},
eprint = {https://www.eneuro.org/content/6/5/ENEURO.0299-18.2019.full.pdf},
journal = {eNeuro}
}